Measuring Agricultural Productivity Using
the Average Productivity Index (API)1
Lal Mervin Dharmasiri *
Abstract
The concept of agricultural productivity has been extensively
used to explain the spatial organization and pattern of agriculture. Several
academics have been attempting to measure and identify the spatial pattern
of agricultural productivity. This study attempts to formulate a different model
for measuring agricultural productivity. It is named as ‘Average Productivity
Index’ (API) which can identify the spatial distribution pattern of productivity
of a state or a country. Major components of the API, are the average yield
and the harvested area at the country or state level.
The API would be helpful for determining the suitability and
productivity of agricultural crops and for identifying the spatial distribution
pattern because of the components which are used for the calculation. Further,
this model would be useful in demarcating and identifying agricultural
regions. The planners and policy makers will be able to make decisions by
considering the outcome of the API that would lead to better performance in
the agricultural sector.
Key words : Agricultural Productivity - Average Productivity Index Agricultural Regions - Spatial Organization - Spatial Pattern
1. This paper is based on the author's PhD thesis 'Land use, land tenure and agricultural
productivity in Sri Lanka : A spatio-temporal analysis' submitted to the University of
Pune, India in 2009.
* Professor, Department of Geography, University of Kelaniya, Kelaniya, Sri Lanka
e-mail : [email protected]
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Introduction
‘Agricultural Productivity’ has been defined by several scholars
with reference to their own views and disciplines. Agriculturalists,
agronomists, economists and geographers have interpreted it in
different ways. Agricultural productivity is defined in agricultural
geography as well as in economics as “output per unit of input ” or
“output per unit of land area”, and the improvement in agricultural
productivity is generally considered to be the results of a more
efficient use of the factors of production, viz. physical, socioeconomic,
institutional and technological.
Singh and Dhillion (2000) suggested that the “yield per unit”
should be considered to indicate agricultural productivity. Many
scholars have criticized this suggestion pointing out that it considered
only land as a factor of production, with no other factors of production.
Therefore, other scholars have suggested that agricultural productivity
should contain all the factors of production such as labor, farming
experiences, fertilizers, availability and management of water and
other biological factors. As they widely accept that the average return
per unit does not represent the real picture, the use of marginal return
per agricultural unit was suggested.
Agricultural productivity may be defined as the “ratio of index
of local agricultural output to the index of total input used in farm
production” (Shafi, 1984). It is, therefore, a measure of efficiency with
which inputs are utilized in production, if other things being equal.
Agricultural productivity here refers to the returns from arable land
or cultivable land unit. Dewett and Singh (1966) defined "agricultural
efficiency as productivity expressing the varying relationship between
agricultural produce and one of the major inputs, like land, labor or
capital, while other complementary factors remaining the same".
This expression reveals that the productivity is a physical component
rather than a broad concept. Saxon observed that productivity is a
physical relationship between output and the input which gives rise
to that output (Quoted in Saxon, 1965). Considering such different
views, productivity of agriculture has been examined in this paper
from different perspectives, such as productivity of land, labor and
capital.
Productivity of land is a very important factor of agriculture
because it is the most permanent and fixed factor among the three
Measuring Agricultural Productivity Using the Average Productivity Index ...
27
categories of input; land, labor and capital. Basically, land as a unit
basis articulates yield of crop in terms of output to provide the
foodstuff for the nation and secure employment opportunities for
the rural community. Productivity of land may be raised by applying
input packages consisting of improved seeds, fertilizers, agrochemicals and labour intensive methods (Fladby, 1983). And also it
could be raised by applying crop diversification/ multi cropping in a
season on the same land as practised by the farmers of Mahaweli
system ‘H’ area (Dharmasiri, 2008) and by adopting year round mixcropping system on the same land as done by vegetable farmers
of Nuwaraeliya district (Dharmasiri, 2010). Another initiative that can
have the effect of raising land productivity involves ruminants, such
as cattle, sheep and goats. Although rangelands are being grazed
to even exceeding the carrying capacity, there is a large unrealized
potential for feeding agricultural residues to ruminants, which have
a complex digestive system that enables them to convert roughage,
which humans cannot digest into animal protein.
Productivity of labour is important as a determinant of the
income of the population engaged in agriculture. In general, it may
be expressed by the man hours or days of work needed to produce
a unit of production. Shafi (1984) has mentioned that the labour
productivity is measured by the total agricultural output per unit of
labour. It relates to the single most important factor of production,
is intuitively appealing and relatively easy to measure. On the other
hand, labour productivity is a key determinant of living standards,
measured as per capita income, and this perspective is of significant
policy relevance. However, it only partially reflects the productivity of
labour in terms of the personal capacities of workers or the intensity
of their efforts (OECD, 2001). In agricultural geography, the labour
productivity has two major important aspects. First, it profoundly
affects national prosperity and secondly, it principally determines the
standard of living of the agricultural population.
Capital, in terms of purchase of land, development of land,
reclamation of land, drainage, irrigation purpose, livestock, feeds,
seeds, agricultural implements, and machineries, crop production
chemicals is being given priority as a factor for enhancing agricultural
productivity. Jamison and Lau (1982) and Alderman et al. (1996)
have examined the relationship between the level of education and
wage with the crop productivity. A study conducted by Fafchamps and
Quisumbing (1998) has also identified how various facets of human
capital affects the crop productivity in Pakistan.
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Spatial analysis of agricultural productivity is very important
because it can highlight the structure and problems of production
relations on which basis appropriate policies can be suggested by
the policy framers. The concept of agricultural productivity has been
extensively used to explain the spatial organization and pattern of
agriculture. Productivity is generally considered from two directions;
(a) productivity of land and (b) productivity of infrastructure engaged
in agriculture. Productivity of land is closely linked with the productivity
of infrastructure. So, attempts have been made to examine the spatial
differences through the present approach.
Perspectives of Agricultural Productivity
Land is a permanent and fixed factor among other production
factors such as labor and capital. Agricultural productivity of land is
explained by production of crops in terms of output or yield per unit of
land.
The productivity of labour has also taken an important place
in agricultural economics. It is basically an important determinant
of the labor force engaged in agriculture. The productivity of labor
is somewhat a controversial concept than land productivity (Shafi,
1984). Labor input vs. agricultural output is an important parameter
of determining productivity of labor. Total labor force, number of man
hours scarified for farming and market value of labor are very important
factors of labor productivity while considering monetary value added
per man hour or man day. However, agricultural labor productivity may
be enhanced through training, and increase of incentives or wages
etc. Working capital may be utilized in the agricultural production
process. It is generally utilized for the purchase of land, for land
reclamation, drainage, irrigation process, livestock purchase, feeds,
seeds, fertilizers, chemicals, agricultural implements and machinery
(tangible goods) etc. Capital may be an important component for
determining productivity of land, which further refers to enhancing
efficiency of land. Efficiency refers to the properties and qualities of
various inputs, the manner in which they are combined and utilized in
production.
Increase of the tangible capital such as high yielding
varieties, fertilizers, pesticides, herbicides, agricultural instruments
Measuring Agricultural Productivity Using the Average Productivity Index ...
29
and machinery etc., in a systematic manner would be able to
enhance agricultural productivity in any unit of land. But farmer has to
identify the optimum level to maximize farm productivity. Agricultural
productivity is a measure of farming efficiency.
Agricultural productivity is frequently associated with the
attitude towards work, thrift, industriousness and aspirations for
a high standard of living, etc, (Singh and Dhillion, 2000). Some
communities are much more efficient in maintaining a higher level
of farm productivity by their own inherited special characteristics.
In general, agricultural productivity is influenced by several factors,
the major ones being physical, socio-economic and technological.
Earlier the role played by physical factors attracted much interest.
Nowadays, the importance of natural factors has been depleted while
the dynamic factors like technology and socio-economic factors have
come forward. Yet, people have minimal control over the physical
environment such as rain, duration and intensity of sunlight, soil
quality and timing of water availability. There is, therefore, no single
goal that can be set for all situations in terms of highest productivity.
However, attempts are being made to control some of the physical
factors by using technology. Increasing soil quality by adding chemical
fertilizers, farming by irrigable water, controlling pests by chemicals
and increasing production by high yielding varieties (HYV) are some of
the achievements of the present generation. In developing countries,
using poor farm technology still results in low land productivity. As a
result, difference between farmers using advanced farm technology
and those not using it has today acquired a social significance.
Yet, the climax of agricultural productivity of farmers is far off in the
developing countries while some developed nations have gone far
ahead in this context.
Measuring Agricultural Productivity
Agricultural development of a country or region is closely
related with production of the crops. From time to time, considerable
efforts have been made to increase the production and productivity
level. The measurement of agricultural productivity helps in knowing
the areas that are performing rather less or higher efficiency
in comparison with the nearby areas. By considering the facts,
agricultural development plans may be formulated to overcome the
regional inequalities. It also provides an opportunity to ascertain the
ground reality, the real cause of agricultural backwardness of an area.
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Several scholars have attempted to quantify the agricultural
productivity. Kendall introduced Ranking coefficient for measuring
agricultural productivity in 1939. Stamp (1958) also used Kendall’s
ranking coefficient for international comparisons. In 1964, Enyedi
devised new techniques for determining an Index of productivity
coefficient of agriculture. J.L. Buck developed a new technique, which
related to grain equivalents per head of production. The index was
known as Grain equivalents index. It was further modified by E.de
Vries in 1967 (Quoted in Singh and Dhillion, 2000). Bhatia introduced
a Productivity evaluation index in 1967. He considered that all
physical and human factors join in to produce the agricultural crops.
Sapre and Deshpande (1964) have introduced a Weighted rank
index for measuring agricultural productivity. Agricultural productivity
coefficient index was introduced by Shafi in 1984 by using calorie
values relating to each crop. In 1972, Jasbir Singh attempted to
introduce a new technique for calculating agricultural efficiency by
expressing the per unit area carrying capacity. Hussain also developed
a technique to measure agricultural productivity in 1976 (Hussain,
1976). He converted agricultural production into monetary values
of a regional unit in production. Kawagoe and others have used a
method of Production function approach for measuring agricultural
productivity among different countries (Kawagoe et al. 1985). In
2005, Vanloon, Patil and Hugar developed an indicator for measuring
crop productivity by using primary product yield or conventional
yield. Dharmasiri (2009) has attempted to measure the agricultural
productivity in Sri Lanka by using Cobb-Douglas Function. These are
some of the methods for measuring agricultural productivity. They
have devised different formulae with different components. Each
model has different data requirements and is suitable for addressing
different questions and has strengths and weaknesses.
Apart from these methodologies, there are three different types
of economic models that have been used for measuring agricultural
productivity: (1) growth accounting technique, (2) econometric
estimation of production relationships and (3) nonparametric models.
Each model can be used to measure aggregate agricultural output.
Each model has different data requirements and is suitable for
addressing different questions and has strengths and weaknesses.
Growth accounting technique involves compiling detailed accounts of
inputs and outputs, aggregating them into input and output indices to
calculate a Total Factor Productivity (TFP) index. Goksel and Ozden
(2007) have applied the TFP with Cobb-Douglas production function
Measuring Agricultural Productivity Using the Average Productivity Index ...
31
in agriculture to analyze the agricultural productivity in Turkey.
The Cobb-Douglas production function (Cobb and Douglas, 1928)
which will be utilized in this analysis is widely used to represent the
relationship of an output to inputs i.e. input-output relationship.
Nonparametric models use linear programming techniques
to calculate TFP. An advantage of the nonparametric approach is that
it does not impose restrictive assumptions on production technology.
The major disadvantage is that since the models are not statistical,
they cannot be statistically tested or validated.
The econometric estimation of production relationships,
which will be applied in this analysis is based on either the
“production function” or the “cost function”. An advantage of this
model is that it permits quantifying the marginal contribution of each
input to aggregate production. For example, one can determine the
impact of one-percent increase in fertilizer use on overall agricultural
production, holding all other inputs constant. Many researchers use
the Cobb-Douglas production function, despite some of its limitations.
Jorgenson et al. (1987) used a cost function approach for each major
sector of the US economy to estimate rates of sectoral productivity
growth and concluded that productivity growth has been more rapid in
agriculture than in other sectors. Lewis et al. (1988) used a production
function approach to calculate productivity growth rates for agriculture
and for the reminder of the Australian economy (industry plus service)
and concluded that the rate of productivity growth in agriculture had
been higher than for the reminder of the economy.
All these three models have strengths and weaknesses.
The use of growth accounting technique imposes several strong
assumptions about technology. A disadvantage is that the statistical
methods cannot be used to evaluate their reliability.
The econometric model e.g. Cobb-Douglas function has
the advantage of permitting hypothesis testing and calculation of
confidence intervals to test the reliability of the estimations. This model
clearly measures the marginal contribution of each input to aggregate
agricultural output. If the functional form is more flexible, a further
advantage is that fewer restrictive assumptions about technology are
imposed. A disadvantage of the econometric model is that it requires
more data than the other models.
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
By considering the given facts, different methodologies for
measuring agricultural productivity give dissimilar results. Each and
every formula has inherited weaknesses. Therefore, the attempts
have been made in this paper to apply a different methodology for
measuring agricultural productivity.
Methodology and Justification
The present study attempts to formulate a different model
for measuring agricultural productivity. It is named as Average
Productivity Index (API) which can identify the spatial distribution
pattern of productivity of a state or a country. Major components of
the API, are the average yield and the harvested area at the country
or state level. Productivity is determined by several physical and nonphysical factors. The researcher has used two variables i.e. yield and
harvested area of the selected crops which are the major components
of productivity of the present study. To calculate API the following
formula is used.
First, deviations of selected yields of the crop/ harvested
areas are calculated. Then, the deviations of each crop/ harvested
area should be divided by the standard deviations of each crop/
harvested area and powered the calculated values for getting positive
figures. Since the productivity is a spatial phenomenon, the standard
deviation gives a clear spatial productivity pattern of the land. Thirdly,
coefficient should be calculated by adding all values of each crop/
harvested area together. Finally, the API can be calculated by
multiplying the yield coefficient and the harvested coefficients. For the proceeding analysis, ten food crops including staple
food and other types of crops such as paddy, kurakkan (type of
millet), maize, manioc, green gram, sweet potatoes, potatoes, green
chilies and onions were selected. Selection criteria of the crops are
(i) the total annual production exceeding 10,000 metric tones (MT)
per season and (ii) plantations were avoided due to non-availability
of district level data. For the analysis, the district level data on yield
and cultivated area in selected principle food crops for three decades
were obtained from the Statistical Abstract of the Department of
Census and Statistics in 2002/2003. Measuring Agricultural Productivity Using the Average Productivity Index ...
33
x
X(xi1)
= average yield of each crop in a
district/ unit,
SD(xi1)
= standard deviation of each crop
yield in a district/ unit
y1j
= average harvested extent of each
crop in the district/ unit
SD (yi1) = standard deviation of harvested
extent of each crop in a district/ unit
Yij Where, = average harvested extent of each
crop in a country/state
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Figure 1: Agricultural Productivity Ranks
Mean Productivity
On the basis of the properties of the normal curve relating to
the proportion of the area lying at several magnitudes of distribution for
agricultural productivity were decided (Figure 1). In order to classify
districts into five classes i.e. very high (VH), high (H), medium (M),
low (L) and very low (VL) on the basis of variation of districts around
the mean value of the productivity index, the following method was
applied. The process was able to sustain a uniformity of all the values
of parameters.
This procedure for identifying the levels of productivity
is followed by API methods for the year in 2002/ 2003 seasons
separately. In order to classify districts according to the magnitude of
spatial variation, a uniform method of regional demarcation is worked
out which is based on the API. In order to classify districts/ areas into
five classes on the basis of variation of districts around the mean value
of the productivity index, a method was applied as shown in Table 1.
Measuring Agricultural Productivity Using the Average Productivity Index ...
35
Table 1 : Range of Classes According to Probability Percentage
Probability
Percentage
Range of Index
Grade
Very
(VH)
87.5% and above
Mean + (1.15 SD) and above
62.5% to 87.5%
Mean + (1.15 SD) to Mean +
High (H)
(0.67SD)
37.5% to 62.5%
Mean +(0.67
(0.67SD)
12.5% to 37.5%
Mean - (0.67 SD) to Mean - (1.15
Low (L)
SD)
Below 12.5%
Mean - (1.15 SD) and less
SD)
to
Mean
-
High
Medium (M)
Very Low (VL)
Source: Compiled by the Author
Agricultural Productivity in Sri Lanka
Table 2 was compiled from the harvested extent and
average yield of the selected crops in Yala and Maha seasons in
2002. According to the Table, there were 3 districts with very high
productivity category i.e. Jaffna, Mannar and Mahaweli ‘H’ area in
the yala season. The major reason for the very high productivity
of these areas is the higher average yield values of major crops in
Table 4 shows the levels of agricultural productivity during the maha
season in 2002/2003 in Sri Lanka. Figure 3, illustrates the changing
spatial productivity pattern in Maha season. Numbers of very high,
high and moderate level districts have increased from 18 to 20.
Monaragala and Anuradhapura districts have recorded very high
level of productivity for Maha Season. In 2002, Jaffna district has
reported the highest average yield of sweet potato and higher yield
of kurakkan and manioc while Mannar district reported the highest
average yield of manioc and green chilies. Due to the increase of
market price of chilies in early 2000, many farmers cultivated green
chilies to get higher income. As a result, cultivated area of green
chilies has gone up. The Mahaweli ‘H’ area which is a good example
of this process has recorded the highest productivity.
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
2.68
2.82
Gampaha
Hambanthota
6.16
9.64
5.53
5.97
2.46
Kalutara
Kandy
Kegalle
Kilinochchi
Kurunegala
17.23
3.24
Galle
Jaffna
2.98
Colombo
7.58
Badulla
13.08
6.52
Anuradhapura
Batticaloa
6.30
Ampara
Districts
Coefficient of
Yield
Yala
Seoson
35.58
2.66
4.22
3.88
0.78
9.50
3.44
3.27
1.02
2.17
2.68
18.75
5.86
18.46
A
87.68
15.87
23.34
37.39
4.83
163.72
9.71
8.77
3.32
6.46
35.01
142.17
38.24
116.27
B
M
M
M
M
L
VH
M
L
L
L
M
H
M
M
Rank
5.25
7.11
4.34
9.77
8.21
12.06
4.32
7.30
1.98
0.83
10.98
6.92
5.54
6.98
Coefficient
of Yield
Maha
Seoson
18.37
3.49
2.33
2.32
0.44
8.49
13.17
3.25
0.51
2.32
3.11
13.65
43.96
18.77
A
96.48
24.79
10.11
22.70
3.58
102.42
56.92
23.74
1.00
1.93
34.20
94.38
243.36
79.031
B
M
M
cont'd
VL
M
VL
H
M
M
VL
VL
M
M
VH
H
Rank
Table 2: Average Productivity Index (API) and Ranking of Districts Yala and Maha Seasons 2002/2003
Measuring Agricultural Productivity Using the Average Productivity Index ...
37
19.00
19.07
3.86
3.82
10.48
7.10
6.27
2.23
4.36
11.22
3.11
9.47
7.47
362.31
39.49
12.90
59.32
54.06
70.26
11.01
29.48
50.58
12.03
52.56
157.45
B
VH
M
L
M
M
M
L
M
L
L
M
VH
Rank
20.53
16.87
2.17
6.21
6.12
13.03
8.45
14.38
8.99
5.08
8.08
8.47
Coefficient
of Yield
Maha
Seoson
2.13
7.94
5.87
4.91
4.74
5.01
3.63
7.38
3.54
2.67
2.77
25.28
A
100.70
80.01
10.90
22.54
45.16
46.15
22.52
39.80
227.32
10.81
64.15
49.69
B
Source: Compiled by the Author
H= High and VH=Very High
A= Coefficients of Harvested Area, B= API Rank (Level of Productivity): VL= Very Low, L=Low, M=Medium,
10.23
3.38
Trincomalee
‘H’ area
5.66
Ratnapura
Vavniya
7.62
Puttalam
6.77
Mullaitivu
4.94
4.51
Monaragala
11.21
3.87
Matara
Polonnaruwa
5.55
Matale
A
M
M
VL
M
M
M
M
M
VH
VL
M
M
Rank
Nuwaraeliya
21.08
Coefficient of
Yield
Yala
Seoson
Mannar
Districts
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Measuring Agricultural Productivity Using the Average Productivity Index ...
39
There was only one district with high productivity figures and
there were 18 districts with medium level of productivity in the Yala
season. Interestingly, all these districts are spatially located in the
Dry zone (see; Figure 2). Inadequate rainfall in the Dry zone may
be the cause for the medium level of productivity during the season.
However, many farmers cultivate other field crops such as kurakkan,
maize and cowpea etc, mainly with the water supply from small tanks
in the Dry zone of the country. Another seven districts reported low
level of productivity in this season. Most of these districts are located
in the Western part of the country which do not have better agricultural
prospects.
There were eight districts or 30 per cent of the districts
which have reported lower level of agricultural productivity during the
Yala, 2002. They are Colombo, Galle, Gampaha, Kalutara, Matara,
Monaragala, Tricomalee and Nuwaraeliya. Genarally, Nuwaraeliya
district records better agricultural productivity in both seasons. But in
Yala, 2002 it recorded of lower average yield of many crops such as
kurakkan, maize, cowpea and green chilies.
Table 3: Agricultural Productivity Categories Based on the API
Yala Season 2002
Ranking
Coefficient
Over 150.77
113.59 – 150.76
9.71 – 113.58
Number
of
Districts
Percentage of Total
Number of Districts in
the Country
Very High
3
11.53
High
1
3.86
Medium
14
53.85
Productivity
Grade
-23.37– 9.70
Low
8
30.77
Below -23.36
Very Low
--
--
Source: Compiled by the Author
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Table 4: Agricultural Productivity Categories Based on the API
2002/03, Maha Season
Ranking
Coefficient
Over 132.51
Productivity
Grade
Number
of
Districts
Percentage of Total
Number of Districts
in the Country
Very High
2
7.69
102.34 – 132.50
High
2
7.69
18.13 – 102.33
Medium
16
61.54
12.03 – 18.12
Low
--
--
Very Low
6
23.08
Below 12.03
Source: Compiled by the Author
Table 4 shows the levels of agricultural productivity during
the maha season in 2002/2003 in Sri Lanka. Figure 3, illustrates the
changing spatial productivity pattern in maha season. Numbers of
very high, high and moderate level districts have increased from 18 to
20. Monaragala and Anuradhapura districts have recorded the highest level of productivity because of very high extent of harvest. On
the other hand, the number of districts in low productivity category
has shown a decrease due to the low extent of harvest due to inadequate rainfall during the period. The high standard deviations (62.85)
of the average yield values and harvested extent (60.24) have been
responsible for low productivity in the maha season.
Figures 2 and 3 clearly indicate that most areas of Sri Lanka
have a good potential for developing agriculture. The districts which
recorded the high and moderate level of productivity under the API
have a better prospect for cultivating other field crops to increase
the production and productivity of the country. Increase of agricultural
production would help to meet the food demand of the nation and will
save foreign exchange required for imports.
Conclusions
The forgoing section of this article has analyzed the spatial
difference of all the administrative districts in Sri Lanka for the year
of 2002 by the API. Although the productivity differentiation cannot
be precisely demarcated by administrative boundaries, the results
of applying the API provide with a general picture of the spatial
differentiation in agricultural productivity. It can also be assumed that
Measuring Agricultural Productivity Using the Average Productivity Index ...
41
the border areas of districts represent a mix up picture of agricultural
productivity. The integration of productivity variation maps for Yala and
Maha seasons provides that, over 70 percent of the Dry zone areas
of the country have achieved a moderate level of productivity during
the Yala season while it reached to higher level in Maha season.
Besides a larger area of agricultural lands are being cultivated during
the Maha season and could reap a huge volume of production,
particularly in paddy. The resulting pattern of agricultural productivity
shows some correlation with the major geographical factors when
compared with the soil and rainfall distribution map of Sri Lanka.
In spite of this pattern, there are some deviations of
productivity due to availability of irrigation facilities. Moderate level
of agricultural productivity can be identified where the small and
medium size irrigation systems are being operated. It is peculiar to
see that the South-Western quadrant of the country does not show
better prospects in terms of agricultural productivity depending on
the variables applied for this study. However, the situation may
be different if cash crops such as cinnamon and rubber were
considered. The North Eastern parts of the country do not show
a potential for high level of agricultural productivity due to failure
of retaining adequate groundwater for successful cultivation to meet
crop water requirements.
However, this situation has been overcome by some farmers
who have applied tapped deep water. The potential of increasing
agricultural production in the South Eastern part of the country seems
to be a very high due ample land area available for further extension
of cultivation. Besides a vast land area in the region has been
demarcated as reserved forest. The Central highland of the county
does not demonstrate a high level of productivity based on the grain
yield per unit as applied in this study. The productivity picture may be
different if the variable of plantation crops like tea and rubber were
applied. The resultant pattern of spatial distribution of the country
thus gives some guidance in identification of potential agricultural
development regions of the country that enable the policy makers to
decide on future scenarios of agricultural growth.
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Sri Lanka Journal of Advanced Social Studies Vol. 1 - No.2
Discussion
The API attempted to examine the productivity by considering
two major components of the productivity namely, the average yield
and the harvested area related to the selected crops. The level of
productivity is identified according to the calculated coefficient values
of the components. Average production of any crop in any given area
is determined by several geographical and non-geographical factors.
However, mainly the geographical factors influence on the cultivated
and harvested extent. The API would be helpful for determining the
suitability and productivity of agricultural crops and for identifying the
spatial distribution pattern because of the components which are
used for the calculation. It is an important fact that the API deals with
the average and standard deviation. The standard deviation helps to
understand the variation from the mean that can be used to generalize
the agricultural performance.
This method may be useful in demarcating and identifying
agricultural regions. Further, the planners and policy makers will
be able to make decisions that would lead to better performance in
agricultural sector.
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